Refactor access to trainer attributes in LightningModule

CHANGELOG.md

@@ -107,6 +107,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Changed the default value for the `progress_bar_refresh_rate` Trainer argument in Google COLAB notebooks to 20 ([#5516](https://github.com/PyTorchLightning/pytorch-lightning/pull/5516))
 
 
+- Made `LightningModule.global_rank`, `LightningModule.local_rank` and `LightningModule.logger` read-only properties ([#5730](https://github.com/PyTorchLightning/pytorch-lightning/pull/5730))
+
+
 - Refactored Accelerators and Plugins 
     * Added base classes for plugins ([#5715](https://github.com/PyTorchLightning/pytorch-lightning/pull/5715))
     * Added parallel plugins for DP, DDP, DDPSpawn, DDP2 and Horovod ([#5714](https://github.com/PyTorchLightning/pytorch-lightning/pull/5714))

pytorch_lightning/core/lightning.py

@@ -67,6 +67,9 @@ class LightningModule(
         "current_epoch",
         "global_step",
         "running_stage",
+        "global_rank",
+        "local_rank",
+        "logger",
     ] + DeviceDtypeModuleMixin.__jit_unused_properties__
 
     def __init__(self, *args, **kwargs):
@@ -83,9 +86,6 @@ def __init__(self, *args, **kwargs):
         #: Pointer to the trainer object
         self.trainer = None
 
-        #: Pointer to the logger object
-        self.logger = None
-
         self._distrib_type = None
         self._device_type = None
 
@@ -132,6 +132,16 @@ def global_step(self) -> int:
         """Total training batches seen across all epochs"""
         return self.trainer.global_step if self.trainer else 0
 
+    @property
+    def global_rank(self) -> int:
+        """ The index of the current process across all nodes and devices. """
+        return self.trainer.global_rank if self.trainer else 0
+
+    @property
+    def local_rank(self) -> int:
+        """ The index of the current process within a single node. """
+        return self.trainer.local_rank if self.trainer else 0
+
     @example_input_array.setter
     def example_input_array(self, example: Any) -> None:
         self._example_input_array = example
@@ -163,6 +173,11 @@ def automatic_optimization(self) -> bool:
     def automatic_optimization(self, automatic_optimization: bool) -> None:
         self._automatic_optimization = automatic_optimization
 
+    @property
+    def logger(self):
+        """ Reference to the logger object in the Trainer. """
+        return self.trainer.logger if self.trainer else None
+
     def print(self, *args, **kwargs) -> None:
         r"""
         Prints only from process 0. Use this in any distributed mode to log only once.

pytorch_lightning/trainer/connectors/model_connector.py

@@ -17,6 +17,7 @@
 Currently supports training on CPU, GPU (dp, ddp, ddp2, horovod) and TPU.
 
 """
+from weakref import proxy
 
 
 class ModelConnector:
@@ -30,17 +31,14 @@ def copy_trainer_model_properties(self, model):
         self.trainer.train_loop.automatic_optimization = automatic_optimization
 
         for m in [model, ref_model]:
-            m.trainer = self.trainer
-            m.logger = self.trainer.logger
+            m.trainer = proxy(self.trainer)
             m._device_type = str(self.trainer._device_type)
             m._distrib_type = str(self.trainer._distrib_type)
             m.use_amp = self.trainer.amp_backend is not None
             m.testing = self.trainer.testing
             m.tpu_local_core_rank = self.trainer.tpu_local_core_rank
             m.tpu_global_core_rank = self.trainer.tpu_global_core_rank
             m.precision = self.trainer.precision
-            m.global_rank = self.trainer.global_rank
-            m.local_rank = self.trainer.local_rank
 
     def get_model(self):
         return self._get_reference_model(self.trainer.model)

pytorch_lightning/tuner/tuning.py

@@ -50,12 +50,10 @@ def tune(self, model, train_dataloader, val_dataloaders, datamodule):
                 val_dataloaders=val_dataloaders,
                 datamodule=datamodule,
             )
-            model.logger = self.trainer.logger  # reset logger binding
 
         # Run learning rate finder:
         if self.trainer.auto_lr_find:
             self.internal_find_lr(model)
-            model.logger = self.trainer.logger  # reset logger binding
 
     def scale_batch_size(
             self,

tests/core/test_lightning_module.py

@@ -11,16 +11,68 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from unittest.mock import patch
+from unittest.mock import patch, Mock, PropertyMock
 
 import pytest
 from torch.optim import Adam, SGD
 
 from pytorch_lightning import Trainer
+from pytorch_lightning.loggers import TensorBoardLogger
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.base import BoringModel
 
 
+def test_property_current_epoch():
+    """ Test that the current_epoch in LightningModule is accessible via the Trainer. """
+    model = BoringModel()
+    assert model.current_epoch == 0
+
+    trainer = Mock(current_epoch=123)
+    model.trainer = trainer
+    assert model.current_epoch == 123
+
+
+def test_property_global_step():
+    """ Test that the global_step in LightningModule is accessible via the Trainer. """
+    model = BoringModel()
+    assert model.global_step == 0
+
+    trainer = Mock(global_step=123)
+    model.trainer = trainer
+    assert model.global_step == 123
+
+
+def test_property_global_rank():
+    """ Test that the global rank in LightningModule is accessible via the Trainer. """
+    model = BoringModel()
+    assert model.global_rank == 0
+
+    trainer = Mock(global_rank=123)
+    model.trainer = trainer
+    assert model.global_rank == 123
+
+
+def test_property_local_rank():
+    """ Test that the local rank in LightningModule is accessible via the Trainer. """
+    model = BoringModel()
+    assert model.local_rank == 0
+
+    trainer = Mock(local_rank=123)
+    model.trainer = trainer
+    assert model.local_rank == 123
+
+
+def test_property_logger(tmpdir):
+    """ Test that the logger in LightningModule is accessible via the Trainer. """
+    model = BoringModel()
+    assert model.logger is None
+
+    logger = TensorBoardLogger(tmpdir)
+    trainer = Mock(logger=logger)
+    model.trainer = trainer
+    assert model.logger == logger
+
+
 def test_automatic_optimization(tmpdir):
     class TestModel(BoringModel):
         def optimizer_step(self, *_, **__):

tests/trainer/test_lr_finder.py

@@ -90,6 +90,8 @@ def test_trainer_reset_correctly(tmpdir):
         assert attributes_before[key] == attributes_after[key], \
             f'Attribute {key} was not reset correctly after learning rate finder'
 
+    assert model.trainer == trainer
+
 
 @pytest.mark.parametrize('use_hparams', [False, True])
 def test_trainer_arg_bool(tmpdir, use_hparams):